This invention is particularly useful for controlling plants and processes of various kinds, where some regularly occurring disturbance affects the output of the plant or process. As the ordinary worker in the field of controls will readily recognize the invention described herein as application to any field of control technology from heating, ventilation and air conditioning systems to chemical processing plants, navigational systems, and so on.
A method is described which provides, in the preferred embodiment, using a model based predictive control framework to improve regulatory performance, particularly those processes that are subject to periodic or cyclic disturbances.
Such examples might include temperature disturbances affecting distillation columns (in oil refineries) due to ambient temperature variations over the course of a day (solar cycle), continuous grinding mills subject to feed disturbances near feed-bin changeover times, etc.
Perhaps the primary benefit to using a model that predicts disturbances in feeding this information into the control loop is that processes which are controlled in this way can perform more closely to their constraints. Using the example of a continuous grinding mill, the neural network would be trained on plant data indicating that (using this example) required power consumption to the grinders occurs each time a new ore car is unloaded into the process. Based on the grinding rate, or the size of the ore car, or some other criteria known to the plant operator, a neural net can be trained to expect the occurrence in the change in power requirement for the grinder.
There are many other examples which could be cited for using inventions of this type to improve the performance of the process under control and increase yield by allowing operation of the plant close to the equipment constraints.
Model-based predictive control (MPC) techniques have gained widespread acceptance in the process industry over the past decade due to their ability to achieve multi variable control objectives in the presence of dead time, process constraints and modeling uncertainties. A good review of various MPC algorithms can be found in "Model Predictive Control: Theory and Practice--A Survey", Automatica 23 (3), (1989), Garcia, Pret, & Morari. In general, these algorithms can be considered optimal control techniques which compute control moves as a solution to an optimization problem to minimize an error subject to constraints, either user imposed or system imposed.
In general, an MPC algorithm can be described with reference to the multivariable process. For example, one modeled by the equations: EQU x=f(x,u) (1a) EQU y=g(x,u) (1b),
wherein x is the state variable vector, u is the manipulated variable vector and y is the output variable vector.
There are two broad steps: a prediction step and an optimization step. In the prediction step, at every time step (k), the model is used to predict the plant output over a number of future intervals. This is called the prediction horizon. This prediction is corrected by adding the difference in the outputs from the plant and the model to the predicted output. The predicted output is then subtracted from the desired output trajectory over the prediction horizon to give the predicted error. In the optimization step, the minimization of the predicted error subject to the constraint(s) is performed (usually by a least squares minimization, although other techniques may be used) with the computer control moves being the decision variables. Constraints are specified as direct bounds on the decision variables (called manipulated variable constraints) or as constraint equations, usually based on the process model (output constraints). The first computed control move is implemented on the plant and model, then the steps are repeated for the next time step k. It is within the contemplation of the invention that one or all constraint variables of the one or all constraints are predicted.
It is important to know that in the above procedure, feedback information is utilized. At each and every time step this is used again and again to correct the predictions. However, in conventional MPC schemes it is assumed that the current measured disturbance remains constant over the entire prediction horizon because there is no process information in the future. This is called a constant additive disturbance assumption. It is well known that conventional MPC is a special case of the linear quadratic formulation. Looking at the MPC in a linear quadratic framework, the constant additive disturbance assumption suggests that all disturbances effecting the process are random steps that effect each output independently. In many, if not most, applications, this adversely effects the regulatory performance of a standard MPC controller. To counter this linear quadratic formulation, researchers have used a Kalman filter design to obtain estimates of the states and hence the predicted outputs. A review of known methods for dealing with this is provided in the reference "Model Predictive Control: State of the Art" CPC IV-Proceedings of the 4th International Conference on Chemical Process Control, Padre Island, Tex., pp. 271-296 (1991), Ricker.
A combination of an MPC controller with the neural network as explained in this invention provides for a substantial improvement in overall controller design. It may be referred to herein as a Hybrid controller which may include other controller types besides the MPC), or a Hybrid MPC controller.